Title :
Modeling and Prediction of Vehicle Tube Hydraulic Shock Absorbers Based on BP Neural Network
Author :
Pan, Dong ; Pan, Shuang-xia ; Wang, Wei-rui
Author_Institution :
Inst. of Mech. Design, Zhejiang Univ., Hangzhou
Abstract :
Research on modeling the tube hydraulic shock absorbers is always a challenging issue. This paper presents a modeling method through BP (back-propagation) neural network established by training data from experiments. Characteristic parameters of the absorbers are as the inputs of the BP network model, while damping forces as outputs. Numerical simulations are given as examples, which demonstrate that the method is effective to predict the performance of the absorber successfully
Keywords :
backpropagation; damping; learning (artificial intelligence); shock absorbers; vehicles; BP neural network; training data; vehicle tube hydraulic shock absorber; Cybernetics; Damping; Machine learning; Mathematical model; Neural networks; Numerical simulation; Pistons; Predictive models; Shock absorbers; Training data; Valves; Vehicles; Vibrations; BP neural network; Shock absorber; model; predict;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259141
